bSLAM navigation of a Wheeled Mobile Robot in presence of uncertainty in indoor environment

In this paper, we propose a behavior-based Simultaneous Localization and Map building (bSLAM) approach to deal with the following navigation problem of a Wheeled Mobile Robot (WMR): the behavior fusion, the uncertainty from measurements and modeling and the WMR control. Considering the multiple control objects, i.e., goal approaching and navigation safety, the behavior-based fuzzy path planner is established to deal with the behavior fusion problem by means of different interpretation of the environment from sensing system. Typically, the uncertainty of measurements together with the incremental error of the WMR self-localization is classified as the SLAM problem. In this research, we further consider the modeling uncertainty comparing with the SLAM problem so that the reduced-order SLAM is theoretically obtained via the variation approach in cope with the slipping and sliding effects. Therefore, the uncertainties are able to be effectively reduced at any motion time instead the time that the WMR revisits the well-known landmark in the SLAM algorithm. The effectiveness and the performance of the proposed bSLAM are verified via experiment. Finally, the results compared with SLAM and bSLAM approach show the error covariance is averagely diminished 26.60% in the complex environment.